Showing papers in "Frontiers in Neuroscience in 2016"

Training Deep Spiking Neural Networks Using Backpropagation.

Abstract: Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Literature Review on Needs of Upper Limb Prosthesis Users.

Abstract: The loss of one hand can significantly affect the level of autonomy and the capability of performing daily living, working and social activities. The current prosthetic solutions contribute in a poor way to overcome these problems due to the limitations of the interfaces adopted for controlling the prosthesis and to the absence of force or tactile feedback which limit the hand grasp capabilities. In order to provide indications for further developments in the prosthetic field to increase user satisfaction rates and therefore to reduce device abandonment, this paper reports a literature review on needs analysis of upper limb prosthesis users, by pointing out the critical aspects of the prosthetic solutions in terms of users satisfaction and activities of daily living they would like to perform with the prosthetic device. A list of requirements for upper limb prostheses is proposed, grounded on the performed analysis on user needs. The defined list of requirements for the prosthetic system aims to provide (i) some guidelines for improving the level of acceptability and usefulness of the prosthesis, by accounting for hand functional and technical aspects; (ii) a possible functional scheme of a PNS-based prosthetic system able to satisfy the emerged user wishes; (iii) some hints for improving the quality of the methods (such as questionnaires) adopted for understanding the user satisfaction with their prosthesis.

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

Abstract: In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors while providing power efficiency of 84,000 GigaOps/s/W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

Assessing Neurocognition via Gamified Experimental Logic: A Novel Approach to Simultaneous Acquisition of Multiple ERPs.

Abstract: The present study describes the development of a neurocognitive paradigm: ‘Assessing Neurocognition via Gamified Experimental Logic’ (ANGEL), for performing the parametric evaluation of multiple neurocognitive functions simultaneously. ANGEL employs an audiovisual sensory motor design for the acquisition of multiple event related potentials (ERPs) - the C1, P50, MMN, N1, N170, P2, N2pc, LRP, P300 and ERN. The ANGEL paradigm allows assessment of ten neurocognitive variables over the course of three ‘game’ levels of increasing complexity ranging from simple passive observation to complex discrimination and response in the presence of multiple distractors. The paradigm allows assessment of several levels of rapid decision making: speeded up response vs response-inhibition; responses to easy vs difficult tasks; responses based on gestalt perception of clear vs ambiguous stimuli; and finally, responses with set shifting during challenging tasks. The paradigm has been tested using 18 healthy participants from both sexes and the possibilities of varied data analyses have been presented in this paper. The ANGEL approach provides an ecologically valid assessment (as compared to existing tools) that quickly yields a very rich dataset and helps to assess multiple ERPs that can be studied extensively to assess cognitive functions in health and disease conditions.

Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks

Abstract: To ensure survival, animals must update the internal representations of their environment in a trial-and-error fashion. Psychological studies of associative learning and neurophysiological analyses of dopaminergic neurons have suggested that this updating process involves the temporal-difference (TD) method in the basal ganglia network. However, the way in which the component variables of the TD method are implemented at the neuronal level is unclear. To investigate the underlying neural mechanisms, we trained domestic chicks to associate color cues with food rewards. We recorded neuronal activities from the medial striatum or tegmentum in a freely behaving condition and examined how reward omission changed neuronal firing. To compare neuronal activities with the signals assumed in the TD method, we simulated the behavioral task in the form of a finite sequence composed of discrete steps of time. The three signals assumed in the simulated task were the prediction signal, the target signal for updating, and the TD-error signal. In both the medial striatum and tegmentum, the majority of recorded neurons were categorized into three types according to their fitness for three models, though these neurons tended to form a continuum spectrum without distinct differences in the firing rate. Specifically, two types of striatal neurons successfully mimicked the target signal and the prediction signal. A linear summation of these two types of striatum neurons was a good fit for the activity of one type of tegmental neurons mimicking the TD-error signal. The present study thus demonstrates that the striatum and tegmentum can convey the signals critically required for the TD method. Based on the theoretical and neurophysiological studies, together with tract-tracing data, we propose a novel model to explain how the convergence of signals represented in the striatum could lead to the computation of TD error in tegmental dopaminergic neurons.

The Temple University Hospital EEG Data Corpus.

Abstract: The electroencephalogram (EEG) is an excellent tool for probing neural function, both in clinical and research environments, due to its low cost, non-invasive nature, and pervasiveness. In the clinic, the EEG is the standard test for diagnosing and characterizing epilepsy and stroke, as well as a host of other trauma and pathology related conditions (Tatum et al., 2007; Yamada and Meng, 2009). In research laboratories, EEG is used to study neural responses to external stimuli, motor planning and execution, and brain-computer interfaces (Lebedev and Nicolelis, 2006; Wang et al., 2013). While human interpretation is still the gold standard for EEG analysis in the clinic, a host of software tools exist to facilitate the process or to make predictive analyses such as seizure prediction. Recently, a confluence of events has underscored the need for robust EEG tools. First, there has been a renewed push via the White House BRAIN initiative to understand neural function and disease (Weiss, 2013). Secondly, there is an increased awareness on brain injury owing to both the influx of injured warfighters and numerous high-profile athletes found to have chronic brain damage (McKee et al., 2009; Stern et al., 2011). And thirdly, a wave of consumer grade scalp sensors has entered the market, allowing end users to monitor sleep, arousal, and mood (Liao et al., 2012). In all these applications, there is a need for robust signal processing tools to analyze the EEG data. Historically, EEG signal processing tools have been devised using either ad hoc heuristic methods, or by training pattern recognition engines on small data sets (Gotman, 1982). These methods have yielded limited results, owing mostly to the fact that brain signals (and EEG in particular) are characterized by great variability, which can only be properly interpreted by building statistical models using massive amounts of data (Alotaiby et al., 2014; Ramgopal et al., 2014). Unfortunately, despite EEG being perhaps the most pervasive modality for acquiring brain signals, there is a severe lack of data in the public domain. For example, the “EEG Motor Movement/Imagery Dataset” (http://www.physionet.org/pn4/eegmmidb/) contains ~1500 recordings of 1 or 2 min duration apiece from 109 subjects (Goldberger et al., 2000; Schalk et al., 2004). The CHB-MIT database contains data from 22 subjects, mostly pediatric (Shoeb, 2009). A database from Karunya University contains 175 16-channel EEGs of duration 10 s (Selvaraj et al., 2014). One of the most extensive databases for supporting epilepsy research is the European Epilepsy Database (http://epilepsy-database.eu/), which contains 250 datasets from 30 unique patients, but sells for €3000. Other databases, such as ieee.org, contain a wealth of data from more invasive modalities such as electrocorticogram, but little or no EEG. This lack of publically available data is ironic considering that hundreds of thousands of EEGs are administered annually in clinical settings around the world. Relatively little of this data is publicly available to the research community in a form that is useful to machine learning research. Massive amounts of EEG data would allow the use of state-of-the-art machine learning algorithms to discover new diagnostics and validate clinical practice. Furthermore, it is desirable that such data be collected in clinical settings, as opposed to tightly controlled research environments, since “clinical-grade” data is inherently more variable with respect to parameters such as electrode location, clinical environment, equipment, and noise. Capturing this variability is critical to the development of robust, high performance technology that has real-world impact. In this work, we describe a new corpus, the TUH-EEG Corpus, which is an ongoing data collection effort that has recently released 14 years of clinical EEG data collected at Temple University Hospital. The records have been curated, organized, and paired with textual clinician reports that describe the patients and scans. The corpus is publicly available from the Neural Engineering Data Consortium (www.nedcdata.org) (Picone and Obeid, 2016).

Alpha-Synuclein Oligomers-Neurotoxic Molecules in Parkinson's Disease and Other Lewy Body Disorders.

Abstract: Adverse intra- and extracellular effects of toxic α-synuclein are believed to be central to the pathogenesis in Parkinson’s disease and other disorders with Lewy body pathology in the nervous system. One of the physiological roles of α-synuclein relates to the regulation of neurotransmitter release at the presynapse, although it is still unclear whether this mechanism depends on the action of monomers or smaller oligomers. As for the pathogenicity, accumulating evidence suggest that prefibrillar species, rather than the deposits per se, are responsible for the toxicity in affected cells. In particular, larger oligomers or protofibrils of α-synuclein have been shown to impair protein degradation as well as the function of several organelles, such as the mitochondria and the endoplasmic reticulum. Accumulating evidence further suggest that oligomers/protofibrils may have a toxic effect on the synapse, which may lead to disrupted electrophysiological properties. In addition, recent data indicate that oligomeric α-synuclein species can spread between cells, either as free-floating proteins or via extracellular vesicles, and thereby act as seeds to propagate disease between interconnected brain regions. Taken together, several lines of evidence suggest that α-synuclein have neurotoxic properties and therefore should be an appropriate molecular target for therapeutic intervention in Parkinson’s disease and other disorders with Lewy pathology. In this context, immunotherapy with monoclonal antibodies against α-synuclein oligomers/protofibrils should be a particularly attractive treatment option.

The Neuroendocrine Regulation of Food Intake in Fish: A Review of Current Knowledge.

Abstract: Fish are the most diversified group of vertebrates and, although progress has been made in the past years, only relatively few fish species have been examined to date, with regards to the endocrine regulation of feeding in fish. In fish, as in mammals, feeding behavior is ultimately regulated by central effectors within feeding centers of the brain, which receive and process information from endocrine signals from both brain and peripheral tissues. Although basic endocrine mechanisms regulating feeding appear to be conserved among vertebrates, major physiological differences between fish and mammals and the diversity of fish, in particular in regard to feeding habits, digestive tract anatomy and physiology, suggest the existence of fish- and species-specific regulating mechanisms. This review provides an overview of hormones known to regulate food intake in fish, emphasizing on major hormones and the main fish groups studied to date.

Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment.

Abstract: The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE) method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer's disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer's and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesized that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer's disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.

Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

Abstract: We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

IGF-I: A key growth factor that regulates neurogenesis and synaptogenesis from embryonic to adult stages of the brain

Abstract: The generation of neurons in the adult mammalian brain requires the activation of quiescent neural stem cells (NSCs). This activation and the sequential steps of neuron formation from NSCs are regulated by a number of stimuli, which include growth factors. Insulin-like growth factor-I (IGF-I) exert pleiotropic effects, regulating multiple cellular processes depending on their concentration, cell type and the developmental stage of the animal. Although IGF-I expression is relatively high in the embryonic brain its levels drop sharply in the adult brain except in neurogenic regions, i.e., the hippocampus (HP) and the subventricular zone-olfactory bulb (SVZ-OB). By contrast, the expression of IGF-IR remains relatively high in the brain irrespective of the age of the animal. Evidence indicates that IGF-I influences NSC proliferation and differentiation into neurons and glia as well as neuronal maturation including synapse formation. Furthermore, recent studies have shown that IGF-I not only promote adult neurogenesis by regulating NSC number and differentiation but also, by influencing neuronal positioning and migration as described during SVZ-OB neurogenesis. In this article we will revise and discuss the actions reported for IGF-I signaling in a variety of in vitro and in vivo models, focusing on the maintenance and proliferation of NSCs/progenitors, neurogenesis and neuron integration in synaptic circuits.

Striatal Circuits as a Common Node for Autism Pathophysiology

Abstract: Autism spectrum disorders (ASD) are characterized by two seemingly unrelated symptom domains-deficits in social interactions and restrictive, repetitive patterns of behavioral output. Whether the diverse nature of ASD symptomatology represents distributed dysfunction of brain networks or abnormalities within specific neural circuits is unclear. Striatal dysfunction is postulated to underlie the repetitive motor behaviors seen in ASD, and neurological and brain-imaging studies have supported this assumption. However, as our appreciation of striatal function expands to include regulation of behavioral flexibility, motivational state, goal-directed learning, and attention, we consider whether alterations in striatal physiology are a central node mediating a range of autism-associated behaviors, including social and cognitive deficits that are hallmarks of the disease. This review investigates multiple genetic mouse models of ASD to explore whether abnormalities in striatal circuits constitute a common pathophysiological mechanism in the development of autism-related behaviors. Despite the heterogeneity of genetic insult investigated, numerous genetic ASD models display alterations in the structure and function of striatal circuits, as well as abnormal behaviors including repetitive grooming, stereotypic motor routines, deficits in social interaction and decision-making. Comparative analysis in rodents provides a unique opportunity to leverage growing genetic association data to reveal canonical neural circuits whose dysfunction directly contributes to discrete aspects of ASD symptomatology. The description of such circuits could provide both organizing principles for understanding the complex genetic etiology of ASD as well as novel treatment routes. Furthermore, this focus on striatal mechanisms of behavioral regulation may also prove useful for exploring the pathogenesis of other neuropsychiatric diseases, which display overlapping behavioral deficits with ASD.

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control.

Abstract: The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

Regulation of Neurogenesis by Neurotrophins during Adulthood: Expected and Unexpected Roles

Abstract: The subventricular zone (SVZ) of the anterolateral ventricle and the subgranular zone (SGZ) of the hippocampal dentate gyrus are the two main regions of the adult mammalian brain in which neurogenesis is maintained throughout life. Because alterations in adult neurogenesis appear to be a common hallmark of different neurodegenerative diseases, understanding the molecular mechanisms controlling adult neurogenesis is a focus of active research. Neurotrophic factors are a family of molecules that play critical roles in the survival and differentiation of neurons during development and in the control of neural plasticity in the adult. Several neurotrophins and neurotrophin receptors have been implicated in the regulation of adult neurogenesis at different levels. Here, we review the current understanding of neurotrophin modulation of adult neurogenesis in both the SVZ and SGZ. We compile data supporting a variety of roles for neurotrophins/neurotrophin receptors in different scenarios, including both expected and unexpected functions.

Motor Imagery during Action Observation: A Brief Review of Evidence, Theory and Future Research Opportunities.

Abstract: Motor imagery (MI) and action observation (AO) have traditionally been viewed as two separate techniques, which can both be used alongside physical practice to enhance motor learning and rehabilitation. Their independent use has been shown to be effective, and there is clear evidence that the two processes can elicit similar activity in the motor system. Building on these well-established findings, research has now turned to investigate the effects of their combined use. In this article, we first review the available neurophysiological and behavioral evidence for the effects of combined action observation and motor imagery (‘AO+MI’) on motor processes. We next describe a conceptual framework for their combined use, and then discuss several areas for future research into AO+MI processes. In this review, we advocate a more integrated approach to AO+MI techniques than has previously been adopted by movement scientists and practitioners alike. We hope this early review of an emergent body of research, along with a related set of research questions, can inspire new work in this area. We are optimistic that future research will further confirm if, how, and when this combined approach to AO+MI can be more effective in motor learning and rehabilitation settings, relative to the more traditional application of AO or MI independently.

Gliotransmitter Release from Astrocytes: Functional, Developmental, and Pathological Implications in the Brain

Abstract: Astrocytes comprise a large population of cells in the brain and are important partners to neighboring neurons, vascular cells, and other glial cells. Astrocytes not only form a scaffold for other cells, but also extend foot processes around the capillaries to maintain the blood-brain barrier. Thus, environmental chemicals that exist in the blood stream could have potentially harmful effects on the physiological function of astrocytes. Although astrocytes are not electrically excitable, they have been shown to function as active participants in the development of neural circuits and synaptic activity. Astrocytes respond to neurotransmitters and contribute to synaptic information processing by releasing chemical transmitters called "gliotransmitters." State-of-the-art optical imaging techniques enable us to clarify how neurotransmitters elicit the release of various gliotransmitters, including glutamate, D-serine, and ATP. Moreover, recent studies have demonstrated that the disruption of gliotransmission results in neuronal dysfunction and abnormal behaviors in animal models. In this review, we focus on the latest technical approaches to clarify the molecular mechanisms of gliotransmitter exocytosis, and discuss the possibility that exposure to environmental chemicals could alter gliotransmission and cause neurodevelopmental disorders.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Abstract: Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain function. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

Abstract: Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

Evaluation of Models of Parkinson's Disease

Abstract: Parkinson’s disease is one of the most common neurodegenerative diseases. Animal models have contributed a large part to our understanding and therapeutics developed for treatment of PD. There are several more exhaustive reviews of literature that provide the initiated insights into the specific models; however a novel synthesis of the basic advantages and disadvantages of different models is much needed. Here we compare both neurotoxin based and genetic models while suggesting some novel avenues in PD modelling. We also highlight the problems faced and promises of all the mammalian models with the hope of providing a framework for comparison of various systems.

Mapping Multiplex Hubs in Human Functional Brain Networks.

Abstract: Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.

Neuropharmacology of New Psychoactive Substances (NPS): Focus on the Rewarding and Reinforcing Properties of Cannabimimetics and Amphetamine-Like Stimulants.

Abstract: New psychoactive substances (NPS) are a heterogeneous and rapidly evolving class of molecules available on the global illicit drug market (e.g smart shops, internet, “dark net”) as a substitute for controlled substances. The use of NPS, mainly consumed along with other drugs of abuse and/or alcohol, has resulted in a significantly growing number of mortality and emergency admissions for overdoses, as reported by several poison centers from all over the world. The fact that the number of NPS have more than doubled over the last 10 years, is a critical challenge to governments, the scientific community, and civil society (UNODC, World Drug Report, 2014; EMCDDA, European Drug Report 2014: Trends and developments). The chemical structure (phenethylamines, piperazine, cathinones, tryptamines, synthetic cannabinoids) of NPS and their pharmacological and clinical effects (hallucinogenic, anesthetic, dissociative, depressant) help classify them into different categories. In the recent past, 50% of newly identified NPS have been classified as synthetic cannabinoids followed by new phenethylamines (17%)(WDR, 2014). Besides peripheral toxicological effects, many NPS seem to have addictive properties. Behavioral, neurochemical, and electrophysiological evidence can help in detecting them. This manuscript will review existing literature about the addictive and rewarding properties of the most popular NPS classes: cannabimimetics (JWH, HU, CP series) and amphetamine-like stimulants (amphetamine, methamphetamine, methcathinone and MDMA analogues). Moreover, the review will include recent data from our lab which links JWH-018, a CB1 and CB2 agonist more potent than Δ9-THC, to other cannabinoids with known abuse potential, and to other classes of abused drugs that increase dopamine signaling in the Nucleus Accumbens (NAc) shell. Thus the neurochemical mechanisms that produce the rewarding properties of JWH-018, which most likely contributes to the greater incidence of dependence associated with “Spice” use, will be described (De Luca et al., 2015a). Considering the growing evidence of a widespread use of NPS, this review will be useful to understand the new trends in the field of drug reward and drug addiction by revealing the rewarding properties of NPS, and will be helpful to gather reliable data regarding the abuse potential of these compounds.

The Eye As a Biomarker for Alzheimer's Disease.

Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder resulting in dementia and eventual death. It is the leading cause of dementia and the number of cases are projected to rise in the next few decades. Pathological hallmarks of AD include the presence of hyperphosphorylated tau and amyloid protein deposition. Currently, these pathological biomarkers are detected either through cerebrospinal fluid analysis, brain imaging or post-mortem. Though effective, these methods are not widely available due to issues such as the difficulty in acquiring samples, lack of infrastructure or high cost. Given that the eye possesses clear optics and shares many neural and vascular similarities to the brain, it offers a direct window to cerebral pathology. These unique characteristics lend itself to being a relatively inexpensive biomarker for AD which carries the potential for wide implementation. The development of ocular biomarkers can have far implications in the discovery of treatments which can improve the quality of lives of patients. In this review, we consider the current evidence for ocular biomarkers in AD and explore potential future avenues of research in this area.

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

Abstract: After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required – a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy across disciplines will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery.

Cannabinoid Type 2 (CB2) Receptors Activation Protects against Oxidative Stress and Neuroinflammation Associated Dopaminergic Neurodegeneration in Rotenone Model of Parkinson's Disease.

Abstract: The cannabinoid type two receptors (CB2), an important component of the endocannabinoid system, have recently emerged as neuromodulators and therapeutic targets for neurodegenerative diseases including Parkinson’s disease (PD). The downregulation of CB2 receptors has been reported in the brains of PD patients. Therefore, both the activation and the upregulation of the CB2 receptors are believed to protect against the neurodegenerative changes in PD. In the present study, we investigated the CB2 receptor-mediated neuroprotective effect of β-caryophyllene (BCP), a naturally occurring CB2 receptor agonist, in, a clinically relevant, rotenone (ROT)-induced animal model of PD. ROT (2.5 mg/kg BW) was injected intraperitoneally (i.p.) once daily for four weeks to induce PD in male Wistar rats. ROT injections induced a significant loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) and DA striatal fibers, following activation of glial cells (astrocytes and microglia). ROT also caused oxidative injury evidenced by the loss of antioxidant enzymes and increased nitrite levels, and induction of proinflammatory cytokines: IL-1β, IL-6 and TNF-α, as well as inflammatory mediators: NF-κB, COX-2, and iNOS. However, treatment with BCP attenuated induction of proinflammatory cytokines and inflammatory mediators in ROT-challenged rats. BCP supplementation also prevented depletion of glutathione concomitant to reduced lipid peroxidation and augmentation of antioxidant enzymes: SOD and catalase. The results were further supported by tyrosine hydroxylase immunohistochemistry, which illustrated the rescue of the DA neurons and fibers subsequent to reduced activation of glial cells. Interestingly, BCP supplementation demonstrated the potent therapeutic effects against ROT-induced neurodegeneration, which was evidenced by BCP-mediated CB2 receptor activation and the fact that, prior administration of the CB2 receptor antagonist AM630 diminished the beneficial effects of BCP. The present study suggests that BCP has the potential therapeutic efficacy to elicit significant neuroprotection by its anti-inflammatory and antioxidant activities mediated by activation of the CB2 receptors.

Alzheimer's Disease and Hippocampal Adult Neurogenesis; Exploring Shared Mechanisms

Abstract: New neurons incorporate into the granular cell layer of the dentate gyrus throughout life. Neurogenesis is modulated by behavior and plays a major role in hippocampal plasticity. Along with older mature neurons, new neurons structure the dentate gyrus, and determine its function. Recent data suggest that the level of hippocampal neurogenesis is substantial in the human brain, suggesting that neurogenesis may have important implications for human cognition. In support of that, impaired neurogenesis compromises hippocampal function and plays a role in cognitive deficits in Alzheimer's disease mouse models. We review current work suggesting that neuronal differentiation is defective in Alzheimer's disease, leading to dysfunction of the dentate gyrus. Additionally, alterations in critical signals regulating neurogenesis, such as presenilin-1, Notch 1, soluble amyloid precursor protein, CREB, and β-catenin underlie dysfunctional neurogenesis in Alzheimer's disease. Lastly, we discuss the detectability of neurogenesis in the live mouse and human brain, as well as the therapeutic implications of enhancing neurogenesis for the treatment of cognitive deficits and Alzheimer's disease.

New Insights in Anorexia Nervosa.

Abstract: Anorexia nervosa (AN) is classically defined as a condition in which an abnormally low body weight is associated with an intense fear of gaining weight and distorted cognitions regarding weight, shape, and drive for thinness. This article reviews recent evidences from physiology, genetics, epigenetics, and brain imaging which allow to consider AN as an abnormality of reward pathways or an attempt to preserve mental homeostasis. Special emphasis is put on ghrelino-resistance and the importance of orexigenic peptides of the lateral hypothalamus, the gut microbiota and a dysimmune disorder of neuropeptide signaling. Physiological processes, secondary to underlying, and premorbid vulnerability factors—the “pondero-nutritional-feeding basements”- are also discussed.

Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

Abstract: This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected -367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

Abstract: Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.

Extracellular vesicles and a novel form of communication in the brain

Abstract: In numerous neurodegenerative diseases, the interplay between neurons and glia modulates the outcome and progression of pathology. One particularly intriguing mode of interaction between neurons, astrocytes, microglia, and oligodendrocytes is characterized by the release of extracellular vesicles that transport proteins, lipids, and nucleotides from one cell to another. Notably, several proteins that cause disease, including the prion protein and mutant SOD1, have been detected in glia-derived extracellular vesicles and observed to fuse with neurons and trigger pathology in vitro. Here we review the structural and functional characterization of such extracellular vesicles in neuron-glia interactions. Furthermore, we discuss possible mechanisms of extracellular vesicle biogenesis and release from activated glia and microglia, and their effects on neurons. Given that exosomes, the smallest type of extracellular vesicles, have been reported to recognize specific cellular populations and act as carriers of very specialized cargo, a thorough analysis of these vesicles may aid in their engineering in vitro and targeted delivery in vivo, opening opportunities for therapeutics.

A bidirectional relationship between executive function and health behavior : evidence, implications, and future directions

Abstract: Physically active lifestyles and other health-enhancing behaviors play an important role in preserving executive function into old age. Conversely, emerging research suggests that executive functions facilitate participation in a broad range of healthy behaviors including physical activity and reduced fatty food, tobacco and alcohol consumption. They do this by supporting the volition, planning, performance monitoring, and inhibition necessary to enact intentions and override urges to engage in health damaging behavior. Here, we focus firstly on evidence suggesting that health-enhancing behaviors can induce improvements in executive function. We then switch our focus to findings linking executive function to the consistent performance of health-promoting behaviors and the avoidance of health risk behaviors. We suggest that executive function, health behavior, and disease processes are interdependent. In particular, we argue that a positive feedback loop may exist whereby health behavior-induced changes in executive function foster subsequent health-enhancing behaviors, which in turn help sustain efficient executive functions and good health. We conclude by outlining the implications of this reciprocal relationship for intervention strategies, the design of research studies, and the study of healthy ageing.